Epithelial deconvolution assessment
Marc Elosua-Bayes
octubre 15, 2021
Introduction
In this script we are going to map Epithelial subtypes onto the Visium slides.
Libraries
library(Seurat)
library(ggpubr)
library(cowplot)
library(dplyr)
library(ggplot2)
library(stringr)
library(readr)
library(SPOTlight)
library(SPATA2)Setting parameters
Loading necessary paths and parameters
set.seed(123)
source(here::here("misc/paths.R"))
source(here::here("utils/bin.R"))
"{epithelium}/{plt_dir}" %>%
glue::glue() %>%
here::here() %>%
dir.create(path = ,
showWarnings = FALSE,
recursive = TRUE)
"{epithelium}/{robj_dir}" %>%
glue::glue() %>%
here::here() %>%
dir.create(path = ,
showWarnings = FALSE,
recursive = TRUE)Set gene dictionary
"{cd4}/gene_dict.R" %>%
glue::glue() %>%
here::here() %>%
source(file = .)Load data
We have 8 different datasets that we are going to analyze separately. The spatial data comes from the script 03-clustering/03-clustering_integration.Rmd while the sc data can be found in Ramon’s scRNAseq analysis: /scratch/devel/rmassoni/tonsil_atlas_private/2-DOWNSTREAM_PROCESSING/results/R_objects/processed_seurat_objects/processed_seurat_objects/tonsil_integrated_with_harmony_scrublet_annotated.rds.
sp_obj <- "{clust}/{robj_dir}/integrated_spatial.rds" %>%
glue::glue() %>%
here::here() %>%
readRDS(file = .)
# Load SPOTlight data
spotlight_ls <- "{epithelium}/{robj_dir}/spotlight_ls_epithelium.rds" %>%
glue::glue() %>%
here::here() %>%
readRDS(file = .)# Single cell data
# sc_obj <- "{decon}/{robj_dir}/tonsil_integrated_with_harmony_scrublet_annotated.rds" %>%
# glue::glue() %>%
# here::here() %>%
# readRDS(file = .)Add annotation
sp_obj[["annotation"]] <- dplyr::case_when(
sp_obj@meta.data$Spatial_snn_res.0.3 == 0 ~ "Inter-follicular zone 1",
sp_obj@meta.data$Spatial_snn_res.0.3 == 1 ~ "T cell zone",
sp_obj@meta.data$Spatial_snn_res.0.3 == 2 ~ "GC",
sp_obj@meta.data$Spatial_snn_res.0.3 == 3 ~ "Epithelial 1",
sp_obj@meta.data$Spatial_snn_res.0.3 == 4 ~ "GC Proliferating",
sp_obj@meta.data$Spatial_snn_res.0.3 == 5 ~ "Epithelial 2",
sp_obj@meta.data$Spatial_snn_res.0.3 == 6 ~ "Inter-follicular zone 2",
sp_obj@meta.data$Spatial_snn_res.0.3 == 7 ~ "Muscle",
)Set color dataframe for the cell types
nm_df <- "{epithelium}/{robj_dir}/epithelium_nm_df.rds" %>%
glue::glue() %>%
here::here() %>%
readRDS(file = .)Add colors to cell types
library(RColorBrewer)
# Define the number of colors you want
nb.cols <- nrow(nm_df)
mycolors <- colorRampPalette(RColorBrewer::brewer.pal(8, "Set2"))(nb.cols)
# https://sashamaps.net/docs/resources/20-colors/
color <- c('#e6194B', '#3cb44b', '#ffe119', '#4363d8', '#f58231', '#911eb4',
'#42d4f4', '#f032e6', '#bfef45', '#fabed4', '#469990', '#dcbeff',
'#9A6324', '#fffac8', '#800000', '#aaffc3', '#808000', '#ffd8b1',
'#000075', '#a9a9a9', '#ffffff', '#000000', '#b00b69')
col_df <- dplyr::bind_cols(nm_df, color = color[1:nrow(nm_df)])Analysis
Preprocess data
decon_mtrx <- spotlight_ls[[2]]
decon_mtrx <- decon_mtrx[, colnames(decon_mtrx) != "res_ss"]
# Set as 0 cell types predicted to be under 1 % of the spot
# decon_mtrx[decon_mtrx < 0.03] <- 0Change column names
new_cn <- data.frame(mod_nm = colnames(decon_mtrx)) %>%
dplyr::left_join(nm_df, by = "mod_nm") %>%
# Central.Mem.PASK. fives some trouble because it only changes between + an -
# negative goes first and distinct solves it automatically
dplyr::distinct() %>%
dplyr::pull(plt_nm)
colnames(decon_mtrx) <- new_cnWe are going to add the deconvolution to the Seurat object.
sp_obj@meta.data <- cbind(sp_obj@meta.data, decon_mtrx)Look at SPOTlight results
Check Topic profiles
nmf_mod_ls <- spotlight_ls[[1]]
nmf_mod <- nmf_mod_ls[[1]]
h <- NMF::coef(nmf_mod)
rownames(h) <- paste("Topic", 1:nrow(h), sep = "_")
topic_profile_plts <- SPOTlight::dot_plot_profiles_fun(
h = h,
train_cell_clust = nmf_mod_ls[[2]])
topic_profile_plts[[2]] +
ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90),
axis.text = ggplot2::element_text(size = 12))
Look at all cells profiles
topic_profile_plts[[1]] +
ggplot2::theme(
axis.text.y = ggplot2::element_blank(),
axis.text.x = ggplot2::element_blank(),
axis.title = ggplot2::element_blank())
Look at cells topic profile
basis_spotlight <- data.frame(NMF::basis(spotlight_ls[[1]][[1]]))
train_labs <- spotlight_ls[[1]][[2]]
colnames(basis_spotlight) <- unique(stringr::str_wrap(train_labs, width = 30))
basis_spotlight[basis_spotlight < 0.0000001] <- 0
DT::datatable(basis_spotlight, filter = "top")Cell type location
Look at the location of each cell type in each slice separately
# Iterate over cell types
ct <- colnames(decon_mtrx)
# Iterate over images
lapply(Seurat::Images(sp_obj), function(nm) {
print(nm)
nm_donor <- id_sp_df %>% dplyr::filter(gem_id == nm) %>% dplyr::pull(donor_id)
# Iterate over cell types
ct_plt_ls <- lapply(ct, function(i) {
tmp_plt <- Seurat::SpatialFeaturePlot(
object = sp_obj,
features = i,
alpha = c(0, 1),
images = nm) +
ggplot2::scale_fill_gradientn(
colors = heat.colors(10, rev = TRUE)) +
ggplot2::scale_alpha(range = c(0, 1)) +
ggplot2::labs(title = stringr::str_wrap(string = i,
width = 25),
fill = "") +
ggplot2::theme(
plot.title = ggplot2::element_text(
hjust = 0.5,
size = 20,
face = "bold"))
return(tmp_plt)
})
(plt_arr <- cowplot::plot_grid(
plotlist = ct_plt_ls,
axis = "trbl",
align = "hv",
ncol = 4))
"{epithelium}/{plt_dir}/cell_type_location_epithelial_{nm_donor}.pdf" %>%
glue::glue() %>%
here::here() %>%
cowplot::save_plot(
filename = .,
plot = plt_arr,
base_height = 25,
base_width = 25)
})## [1] "tarwe1_xott6q"## [1] "c28w2r_7jne4i"## [1] "esvq52_nluss5"## [1] "p7hv1g_tjgmyj"## [1] "gcyl7c_cec61b"## [1] "zrt7gl_lhyyar"## [1] "qvwc8t_2vsr67"## [1] "exvyh1_66caqq"## [[1]]
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Make boxplots for each slice
# Iterate over images
lapply(Seurat::Images(sp_obj), function(nm) {
nm_donor <- id_sp_df %>% dplyr::filter(gem_id == nm) %>% dplyr::pull(donor_id)
# Prepare data for boxplots
metadata_long <- sp_obj@meta.data %>%
# tidyr::pivot_longer(cols = c("annotation"),
# names_to = "stratification_id",
# values_to = "stratification_val") %>%
tidyr::pivot_longer(cols = dplyr::all_of(ct),
names_to = "ct_key",
values_to = "ct_val") %>%
# dplyr::left_join(col_df, by = c("ct_key" = "ct_name")) %>%
dplyr::mutate(ct_val = dplyr::if_else(ct_val > 0.001, ct_val, 0)) %>%
dplyr::filter(gem_id == nm)
# Box plot of cell type proportion between stratified regions showing the unadjusted ANOVA Pvalue
keep_ct <- metadata_long %>%
dplyr::group_by(ct_key) %>%
dplyr::summarise(prop_sum = sum(ct_val)) %>%
dplyr::filter(prop_sum > 0) %>%
dplyr::pull(ct_key)
(bplt <- metadata_long %>%
dplyr::filter(ct_key %in% keep_ct) %>%
dplyr::mutate(
ct_key = stringr::str_wrap(string = ct_key,
width = 30)) %>%
# dplyr::mutate(stratification_val = factor(stratification_val,
# levels = c("Fibrotic", "HER2+/ESR1+",
# "HER2+/ESR1-", "HER2-/ESR1+",
# "HER2-/ESR1-"))) %>%
# dplyr::filter(! plt_name %in% c("CD8 EM-like", "CD4 naive-like")) %>%
ggpubr::ggboxplot(data = .,
x = "annotation",
y = "ct_val",
facet.by = "ct_key",
color = "annotation",
fill = "annotation",
add = "jitter",
scales = "free",
repel = TRUE,
outlier.shape = NA,
alpha = 0.6,
palette = "Set1",
ncol = 5) +
ggplot2::theme(
strip.text = ggplot2::element_text(size = 18, face = "bold"),
axis.text.y = ggplot2::element_text(size = 16),
axis.title.y = ggplot2::element_text(size = 20),
# axis.text.x = element_text(size = 12, angle = 90, vjust = 0.5, hjust = 0.5),
axis.text.x = ggplot2::element_blank(),
legend.text = ggplot2::element_text(size = 18),
legend.title = ggplot2::element_blank(),
strip.background = ggplot2::element_blank()) +
ggplot2::labs(
y = "Proportion",
color = "Regions",
fill = "Regions"))
# bplt <- bplt +
# ggpubr::stat_compare_means(method = "anova", size = 6) +
# ggplot2::scale_y_continuous(
# expand = expansion(mult = c(0, 0.1)),
# labels = function(x) sprintf("%.2f", x))
"{epithelium}/{plt_dir}/strat_bplot_{nm_donor}.pdf" %>%
glue::glue() %>%
here::here() %>%
cowplot::save_plot(
filename = .,
plot = bplt,
base_height = 20,
base_width = 25)
})## [[1]]
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Seurat::DimPlot(
object = sp_obj,
group.by = c("annotation", "sample_id"))
Next we want to look at the piecharts in the UMAP
# Loading libraries
suppressMessages(require(ggplot2))
suppressMessages(require(dplyr))
suppressMessages(require(tibble))
suppressMessages(require(grid))
metadata_ds <- data.frame(sp_obj@meta.data)
colnames(metadata_ds) <- colnames(sp_obj@meta.data)
cell_types_all <- ct
cell_types_interest <- cell_types_all
# If not all cell types are in the cell types of interest we only want to keep those spots which have at least one of the cell types of interest
if (!all(cell_types_all %in% cell_types_interest)) {
metadata_ds <- metadata_ds %>%
tibble::rownames_to_column("barcodeID") %>%
dplyr::mutate(rsum = base::rowSums(.[, cell_types_interest,
drop = FALSE])) %>%
dplyr::filter(rsum != 0) %>%
dplyr::select("barcodeID") %>%
dplyr::left_join(metadata_ds %>% tibble::rownames_to_column("barcodeID"),
by = "barcodeID") %>%
tibble::column_to_rownames("barcodeID")
}
## Preprocess data
umap_coord <- data.frame(sp_obj@reductions$umap@cell.embeddings[, c("UMAP_1", "UMAP_2")]) %>%
tibble::rownames_to_column("barcodeID") %>%
dplyr::inner_join(metadata_ds %>% tibble::rownames_to_column("barcodeID"),
by = "barcodeID")
## Plot spatial scatterpie plot
(umap_pie_plt <- ggplot2::ggplot() +
scatterpie::geom_scatterpie(
data = umap_coord,
ggplot2::aes(
x = UMAP_1,
y = UMAP_2),
# Fill the pie chart
cols = cell_types_all,
pie_scale = 0.15,
# Remove black outline
color = NA) +
ggplot2::scale_fill_manual(
breaks = col_df$plt_nm,
values = col_df$color))
"{epithelium}/{plt_dir}/UMAP_piehcart_integrated.pdf" %>%
glue::glue() %>%
here::here() %>%
cowplot::save_plot(
filename = .,
plot = umap_pie_plt,
base_height = 20,
base_width = 25)Cell type correlation matrix
We look at the cell-type correlation for all the slices together
# se_sub <- subset(sp_obj, subset = gem_id == "esvq52_nluss5")
# se_sub
# se_sub@images <- se_sub@images[Seurat::Images(se_sub) == "esvq52_nluss5"]
(cor_mtrx_ct <- SCrafty::correlation_heatmap(
se = sp_obj,
feats = ct,
assay = "Spatial",
slot = "data") +
ggplot2::labs(
title = "Integrated cell-type correlation matrix"))
"{epithelium}/{plt_dir}/magic_cor-mtrx_cell-type_integrated.pdf" %>%
glue::glue() %>%
here::here() %>%
cowplot::save_plot(
filename = .,
plot = cor_mtrx_ct,
base_height = 9,
base_width = 10)We look at the cell-type correlation for each slices separately
# Iterate over images
lapply(Seurat::Images(sp_obj), function(nm) {
se_sub <- subset(sp_obj, subset = gem_id == nm)
se_sub@images <- se_sub@images[Seurat::Images(se_sub) == nm]
(cor_mtrx_ct <- SCrafty::correlation_heatmap(
se = se_sub,
feats = ct,
assay = "Spatial",
slot = "data") +
ggplot2::labs(
title = "{nm} - Integrated cell-type correlation matrix"))
"{epithelium}/{plt_dir}/cor-mtrx_cell-type_integrated_{nm}.pdf" %>%
glue::glue() %>%
here::here() %>%
cowplot::save_plot(
filename = .,
plot = cor_mtrx_ct,
base_height = 9,
base_width = 10)
})## [[1]]
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sessionInfo()## R version 4.0.1 (2020-06-06)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.7 (Santiago)
##
## Matrix products: default
## BLAS/LAPACK: /scratch/groups/hheyn/software/anaconda3/envs/spatial_r/lib/libopenblasp-r0.3.12.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_ES.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_ES.UTF-8 LC_MESSAGES=en_US.UTF-8 LC_PAPER=es_ES.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] grid parallel stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] tidyr_1.1.3 tibble_3.1.2 Biobase_2.50.0 BiocGenerics_0.36.0 RColorBrewer_1.1-2 SPATA2_0.1.0 SPOTlight_0.1.7 readr_1.4.0 stringr_1.4.0 dplyr_1.0.7 cowplot_1.1.1 ggpubr_0.4.0 ggplot2_3.3.5 SeuratObject_4.0.2 Seurat_4.0.3
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.1 reticulate_1.20 tidyselect_1.1.1 htmlwidgets_1.5.3 Rtsne_0.15 scatterpie_0.1.6 munsell_0.5.0 codetools_0.2-18 ica_1.0-2 DT_0.18 future_1.21.0 miniUI_0.1.1.1 withr_2.4.2 colorspace_2.0-2 highr_0.9 knitr_1.35 rstudioapi_0.13 stats4_4.0.1 SingleCellExperiment_1.12.0 ROCR_1.0-11 ggsignif_0.6.1 tensor_1.5 listenv_0.8.0 NMF_0.23.0 MatrixGenerics_1.2.1 labeling_0.4.2 GenomeInfoDbData_1.2.4 polyclip_1.10-0 farver_2.1.0 rprojroot_2.0.2 parallelly_1.26.0 vctrs_0.3.8 generics_0.1.0 xfun_0.26 R6_2.5.0 doParallel_1.0.16 GenomeInfoDb_1.26.4 bitops_1.0-7 spatstat.utils_2.2-0 DelayedArray_0.16.3 assertthat_0.2.1 promises_1.2.0.1
## [43] scales_1.1.1 gtable_0.3.0 globals_0.14.0 goftest_1.2-2 rlang_0.4.11 splines_4.0.1 rstatix_0.7.0 lazyeval_0.2.2 spatstat.geom_2.2-0 broom_0.7.6 BiocManager_1.30.15 yaml_2.2.1 reshape2_1.4.4 abind_1.4-5 crosstalk_1.1.1 backports_1.2.1 httpuv_1.6.1 tools_4.0.1 gridBase_0.4-7 ellipsis_0.3.2 spatstat.core_2.2-0 jquerylib_0.1.4 ggridges_0.5.3 Rcpp_1.0.6 plyr_1.8.6 zlibbioc_1.36.0 purrr_0.3.4 RCurl_1.98-1.3 ps_1.6.0 rpart_4.1-15 deldir_0.2-3 pbapply_1.4-3 S4Vectors_0.28.1 zoo_1.8-9 SummarizedExperiment_1.20.0 haven_2.4.1 ggrepel_0.9.1 cluster_2.1.0 here_1.0.1 magrittr_2.0.1 data.table_1.14.0 scattermore_0.7
## [85] openxlsx_4.2.3 lmtest_0.9-38 RANN_2.6.1 fitdistrplus_1.1-3 matrixStats_0.59.0 hms_1.1.0 patchwork_1.1.1 mime_0.11 evaluate_0.14 xtable_1.8-4 rio_0.5.26 readxl_1.3.1 IRanges_2.24.1 gridExtra_2.3 compiler_4.0.1 KernSmooth_2.23-18 crayon_1.4.1 htmltools_0.5.1.1 mgcv_1.8-36 later_1.2.0 DBI_1.1.1 corrplot_0.89 tweenr_1.0.2 MASS_7.3-54 Matrix_1.3-4 car_3.0-10 cli_2.5.0 SCrafty_0.1.0 igraph_1.2.6 GenomicRanges_1.42.0 forcats_0.5.1 pkgconfig_2.0.3 rvcheck_0.1.8 registry_0.5-1 foreign_0.8-81 plotly_4.9.4.1 spatstat.sparse_2.0-0 foreach_1.5.1 ggcorrplot_0.1.3 bslib_0.2.5.1 rngtools_1.5 pkgmaker_0.32.2
## [127] XVector_0.30.0 digest_0.6.27 sctransform_0.3.2 RcppAnnoy_0.0.18 spatstat.data_2.1-0 rmarkdown_2.11 cellranger_1.1.0 leiden_0.3.8 uwot_0.1.10 curl_4.3.1 shiny_1.6.0 lifecycle_1.0.0 nlme_3.1-150 jsonlite_1.7.2 carData_3.0-4 viridisLite_0.4.0 fansi_0.5.0 pillar_1.6.1 lattice_0.20-44 fastmap_1.1.0 httr_1.4.2 survival_3.2-11 glue_1.4.2 zip_2.2.0 png_0.1-7 iterators_1.0.13 ggforce_0.3.3 stringi_1.6.2 sass_0.4.0 irlba_2.3.3 future.apply_1.7.0